Most mainstream applications of artificial intelligence (AI) make use of its ability to crunch large volumes of data, detecting patterns and trends within. The results can help predict the future behavior of financial markets and city traffic, and even assist doctors to diagnose disease before symptoms appear.Most mainstream applications of artificial intelligence (AI) make use of its ability to crunch large volumes of data, detecting patterns and trends within. The results can help predict the future behavior of financial markets and city traffic, and even assist doctors to diagnose disease before symptoms appear.Machine learning & AI[#item_full_content]
Google DeepMind researchers this week unveiled a highly accurate AI-based weather prediction model they say marks “a turning point in weather forecasting.”Google DeepMind researchers this week unveiled a highly accurate AI-based weather prediction model they say marks “a turning point in weather forecasting.”[#item_full_content]
Google DeepMind researchers this week unveiled a highly accurate AI-based weather prediction model they say marks “a turning point in weather forecasting.”Google DeepMind researchers this week unveiled a highly accurate AI-based weather prediction model they say marks “a turning point in weather forecasting.”Computer Sciences[#item_full_content]
3D printing is advancing rapidly, and the range of materials that can be used has expanded considerably. While the technology was previously limited to fast-curing plastics, it has now been made suitable for slow-curing plastics as well. These have decisive advantages as they have enhanced elastic properties and are more durable and robust.3D printing is advancing rapidly, and the range of materials that can be used has expanded considerably. While the technology was previously limited to fast-curing plastics, it has now been made suitable for slow-curing plastics as well. These have decisive advantages as they have enhanced elastic properties and are more durable and robust.[#item_full_content]
An artificial sensory system that is able to recognize fine textures—such as twill, corduroy and wool—with a high resolution, similar to a human finger, is reported in a Nature Communications paper. The findings may help improve the subtle tactile sensation abilities of robots and human limb prosthetics and could be applied to virtual reality in the future, the authors suggest.An artificial sensory system that is able to recognize fine textures—such as twill, corduroy and wool—with a high resolution, similar to a human finger, is reported in a Nature Communications paper. The findings may help improve the subtle tactile sensation abilities of robots and human limb prosthetics and could be applied to virtual reality in the future, the authors suggest.Robotics[#item_full_content]
An artificial sensory system that is able to recognize fine textures—such as twill, corduroy and wool—with a high resolution, similar to a human finger, is reported in a Nature Communications paper. The findings may help improve the subtle tactile sensation abilities of robots and human limb prosthetics and could be applied to virtual reality in the future, the authors suggest.An artificial sensory system that is able to recognize fine textures—such as twill, corduroy and wool—with a high resolution, similar to a human finger, is reported in a Nature Communications paper. The findings may help improve the subtle tactile sensation abilities of robots and human limb prosthetics and could be applied to virtual reality in the future, the authors suggest.[#item_full_content]
Several years ago, MIT anthropologist Héctor Beltrán ’07 attended an event in Mexico billed as the first all-women’s hackathon in Latin America. But the programmers were not the only women there. When the time came for the hackathon pitches, a large number of family members arrived to watch.Several years ago, MIT anthropologist Héctor Beltrán ’07 attended an event in Mexico billed as the first all-women’s hackathon in Latin America. But the programmers were not the only women there. When the time came for the hackathon pitches, a large number of family members arrived to watch.[#item_full_content]
AI is everywhere—driving cars, diagnosing illnesses, making credit decisions, ranking job candidates, identifying faces, assessing parolees. These headlines alone should be enough to convince you that AI is far from ethical. Nonetheless, terms like “ethical AI” prevail alongside equally problematic terms like “trustworthy AI.”AI is everywhere—driving cars, diagnosing illnesses, making credit decisions, ranking job candidates, identifying faces, assessing parolees. These headlines alone should be enough to convince you that AI is far from ethical. Nonetheless, terms like “ethical AI” prevail alongside equally problematic terms like “trustworthy AI.”Machine learning & AI[#item_full_content]
A new biopic on the life of Edith Piaf will use artificial intelligence to allow the French star to narrate her own story, Warner Music and her estate said on Tuesday.A new biopic on the life of Edith Piaf will use artificial intelligence to allow the French star to narrate her own story, Warner Music and her estate said on Tuesday.Machine learning & AI[#item_full_content]
Unsupervised domain adaptation has garnered a great amount of attention and research in past decades. Among all the deep-based methods, the autoencoder-based approach has achieved sound performance for its fast convergence speed and a no-label requirement. The existing methods of autoencoders just serially connect the features generated by different autoencoders, which poses challenges for discriminative representation learning and which fails to find the real cross-domain features.Unsupervised domain adaptation has garnered a great amount of attention and research in past decades. Among all the deep-based methods, the autoencoder-based approach has achieved sound performance for its fast convergence speed and a no-label requirement. The existing methods of autoencoders just serially connect the features generated by different autoencoders, which poses challenges for discriminative representation learning and which fails to find the real cross-domain features.[#item_full_content]
